pesuacademy.models package
Submodules
pesuacademy.models.announcement module
Model for announcements in the PESU Academy system.
- class pesuacademy.models.announcement.Announcement(**data)[source]
Bases:
pydantic.main.BaseModelRepresents an announcement in the PESU Academy system.
- Parameters:
title (str)
date (datetime.date)
content (str)
- date
The date of the announcement.
- Type:
- attachments
Optional list of attachment links related to the announcement.
- Type:
Optional[List[str]]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- Return type:
Self
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attachments': FieldInfo(annotation=Union[list[str], NoneType], required=False, default=None), 'content': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=date, required=True), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
pesuacademy.models.course module
Model for courses in the PESU Academy system.
- class pesuacademy.models.course.Attendance(**data)[source]
Bases:
pydantic.main.BaseModelRepresents attendance information for a course.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attended': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'percentage': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'total': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.course.Course(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a course in the PESU Academy system.
- Parameters:
- attendance
Attendance information for the course.
- Type:
Optional[Attendance]
-
attendance:
Attendance|None
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attendance': FieldInfo(annotation=Union[Attendance, NoneType], required=False, default=None), 'code': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
pesuacademy.models.materials module
Model for materials in the PESU Academy system.
- class pesuacademy.models.materials.Unit(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a unit of a course in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.materials.Topic(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a topic within a unit in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'course_id': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True), 'unit_id': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.materials.MaterialLink(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a link to a material in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'is_pdf': FieldInfo(annotation=bool, required=True), 'title': FieldInfo(annotation=str, required=True), 'url': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
pesuacademy.models.profile module
Model for student profile in the PESU Academy system.
- class pesuacademy.models.profile.PersonalDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents personal details of a user in the PESU Academy system.
- Parameters:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'aadhar_no': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'branch': FieldInfo(annotation=str, required=True), 'contact_no': FieldInfo(annotation=str, required=True), 'email_id': FieldInfo(annotation=str, required=True), 'image': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'name_as_in_aadhar': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'pesu_id': FieldInfo(annotation=str, required=True), 'program': FieldInfo(annotation=str, required=True), 'section': FieldInfo(annotation=str, required=True), 'semester': FieldInfo(annotation=str, required=True), 'srn': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.OtherInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents other personal information of a user in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'blood_group': FieldInfo(annotation=str, required=True), 'date_of_birth': FieldInfo(annotation=str, required=True), 'puc_marks': FieldInfo(annotation=str, required=True), 'sslc_marks': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.QualifyingExamination(**data)[source]
Bases:
pydantic.main.BaseModelRepresents details of a qualifying examination in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'exam': FieldInfo(annotation=str, required=True), 'rank': FieldInfo(annotation=str, required=True), 'score': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.ParentDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents details of a parent in the PESU Academy system.
- Parameters:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'designation': FieldInfo(annotation=str, required=True), 'email': FieldInfo(annotation=str, required=True), 'employer': FieldInfo(annotation=str, required=True), 'mobile': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'occupation': FieldInfo(annotation=str, required=True), 'qualification': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.ParentInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents information about parents in the PESU Academy system.
- Parameters:
- father
Details of the father.
- Type:
- mother
Details of the mother.
- Type:
-
father:
ParentDetails
-
mother:
ParentDetails
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'father': FieldInfo(annotation=ParentDetails, required=True), 'mother': FieldInfo(annotation=ParentDetails, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.AddressDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents address details in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'permanent': FieldInfo(annotation=str, required=True), 'present': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.profile.Profile(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a user’s profile in the PESU Academy system.
- Parameters:
personal (pesuacademy.models.profile.PersonalDetails)
other_info (pesuacademy.models.profile.OtherInformation)
qualifying_exam (pesuacademy.models.profile.QualifyingExamination)
- personal
Personal details of the user.
- Type:
- other_info
Other personal information of the user.
- Type:
- qualifying_exam
Details of the qualifying examination.
- Type:
- parents
Information about the user’s parents.
- Type:
- address
Address details of the user.
- Type:
-
personal:
PersonalDetails
-
other_info:
OtherInformation
-
qualifying_exam:
QualifyingExamination
-
parents:
ParentInformation
-
address:
AddressDetails
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'address': FieldInfo(annotation=AddressDetails, required=True), 'other_info': FieldInfo(annotation=OtherInformation, required=True), 'parents': FieldInfo(annotation=ParentInformation, required=True), 'personal': FieldInfo(annotation=PersonalDetails, required=True), 'qualifying_exam': FieldInfo(annotation=QualifyingExamination, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
pesuacademy.models.results module
Model for results in the PESU Academy system.
- class pesuacademy.models.results.Assessment(**data)[source]
Bases:
pydantic.main.BaseModelRepresents an assessment(e.g., ISA1, MATLAB) in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'marks': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.results.Credits(**data)[source]
Bases:
pydantic.main.BaseModelRepresents credits information for a course in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'earned': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.results.CourseResult(**data)[source]
Bases:
pydantic.main.BaseModelRepresents the result of a course in the PESU Academy system.
- Parameters:
code (str)
title (str)
credits (pesuacademy.models.results.Credits | None)
assessments (list[pesuacademy.models.results.Assessment])
- assessments
List of assessments associated with the course.
- Type:
List[Assessment]
-
assessments:
list[Assessment]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'assessments': FieldInfo(annotation=list[Assessment], required=True), 'code': FieldInfo(annotation=str, required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.results.SemesterResult(**data)[source]
Bases:
pydantic.main.BaseModelRepresents the result of a semester in the PESU Academy system.
- Parameters:
sgpa (str)
credits (pesuacademy.models.results.Credits | None)
courses (list[pesuacademy.models.results.CourseResult])
- courses
List of course results for the semester.
- Type:
List[CourseResult]
-
courses:
list[CourseResult]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'courses': FieldInfo(annotation=list[CourseResult], required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'sgpa': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
pesuacademy.models.seating_information module
Model for seating information in the PESU Academy system.
- class pesuacademy.models.seating_information.SeatingInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents seating information in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'block': FieldInfo(annotation=str, required=True), 'course_code': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'terminal': FieldInfo(annotation=str, required=True), 'time': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
Module contents
This module initializes the models for the PESU Academy package.
- class pesuacademy.models.Announcement(**data)[source]
Bases:
pydantic.main.BaseModelRepresents an announcement in the PESU Academy system.
- Parameters:
title (str)
date (datetime.date)
content (str)
- date
The date of the announcement.
- Type:
- attachments
Optional list of attachment links related to the announcement.
- Type:
Optional[List[str]]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attachments': FieldInfo(annotation=Union[list[str], NoneType], required=False, default=None), 'content': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=date, required=True), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.Attendance(**data)[source]
Bases:
pydantic.main.BaseModelRepresents attendance information for a course.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attended': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'percentage': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'total': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.Course(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a course in the PESU Academy system.
- Parameters:
- attendance
Attendance information for the course.
- Type:
Optional[Attendance]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'attendance': FieldInfo(annotation=Union[Attendance, NoneType], required=False, default=None), 'code': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
-
attendance:
Attendance|None
- class pesuacademy.models.MaterialLink(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a link to a material in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'is_pdf': FieldInfo(annotation=bool, required=True), 'title': FieldInfo(annotation=str, required=True), 'url': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.Profile(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a user’s profile in the PESU Academy system.
- Parameters:
personal (pesuacademy.models.profile.PersonalDetails)
other_info (pesuacademy.models.profile.OtherInformation)
qualifying_exam (pesuacademy.models.profile.QualifyingExamination)
- personal
Personal details of the user.
- Type:
- other_info
Other personal information of the user.
- Type:
- qualifying_exam
Details of the qualifying examination.
- Type:
- parents
Information about the user’s parents.
- Type:
- address
Address details of the user.
- Type:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'address': FieldInfo(annotation=AddressDetails, required=True), 'other_info': FieldInfo(annotation=OtherInformation, required=True), 'parents': FieldInfo(annotation=ParentInformation, required=True), 'personal': FieldInfo(annotation=PersonalDetails, required=True), 'qualifying_exam': FieldInfo(annotation=QualifyingExamination, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
-
personal:
PersonalDetails
-
other_info:
OtherInformation
-
qualifying_exam:
QualifyingExamination
-
parents:
ParentInformation
-
address:
AddressDetails
- class pesuacademy.models.SeatingInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents seating information in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'block': FieldInfo(annotation=str, required=True), 'course_code': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'terminal': FieldInfo(annotation=str, required=True), 'time': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.SemesterResult(**data)[source]
Bases:
pydantic.main.BaseModelRepresents the result of a semester in the PESU Academy system.
- Parameters:
sgpa (str)
credits (pesuacademy.models.results.Credits | None)
courses (list[pesuacademy.models.results.CourseResult])
- courses
List of course results for the semester.
- Type:
List[CourseResult]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'courses': FieldInfo(annotation=list[CourseResult], required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'sgpa': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
-
courses:
list[CourseResult]
- class pesuacademy.models.Topic(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a topic within a unit in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'course_id': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True), 'unit_id': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.Unit(**data)[source]
Bases:
pydantic.main.BaseModelRepresents a unit of a course in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.AddressDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents address details in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'permanent': FieldInfo(annotation=str, required=True), 'present': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.OtherInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents other personal information of a user in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'blood_group': FieldInfo(annotation=str, required=True), 'date_of_birth': FieldInfo(annotation=str, required=True), 'puc_marks': FieldInfo(annotation=str, required=True), 'sslc_marks': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.ParentDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents details of a parent in the PESU Academy system.
- Parameters:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'designation': FieldInfo(annotation=str, required=True), 'email': FieldInfo(annotation=str, required=True), 'employer': FieldInfo(annotation=str, required=True), 'mobile': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'occupation': FieldInfo(annotation=str, required=True), 'qualification': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.ParentInformation(**data)[source]
Bases:
pydantic.main.BaseModelRepresents information about parents in the PESU Academy system.
- Parameters:
- father
Details of the father.
- Type:
- mother
Details of the mother.
- Type:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'father': FieldInfo(annotation=ParentDetails, required=True), 'mother': FieldInfo(annotation=ParentDetails, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
-
father:
ParentDetails
-
mother:
ParentDetails
- class pesuacademy.models.PersonalDetails(**data)[source]
Bases:
pydantic.main.BaseModelRepresents personal details of a user in the PESU Academy system.
- Parameters:
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'aadhar_no': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'branch': FieldInfo(annotation=str, required=True), 'contact_no': FieldInfo(annotation=str, required=True), 'email_id': FieldInfo(annotation=str, required=True), 'image': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'name_as_in_aadhar': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'pesu_id': FieldInfo(annotation=str, required=True), 'program': FieldInfo(annotation=str, required=True), 'section': FieldInfo(annotation=str, required=True), 'semester': FieldInfo(annotation=str, required=True), 'srn': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.QualifyingExamination(**data)[source]
Bases:
pydantic.main.BaseModelRepresents details of a qualifying examination in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'exam': FieldInfo(annotation=str, required=True), 'rank': FieldInfo(annotation=str, required=True), 'score': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.Assessment(**data)[source]
Bases:
pydantic.main.BaseModelRepresents an assessment(e.g., ISA1, MATLAB) in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'marks': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
- class pesuacademy.models.CourseResult(**data)[source]
Bases:
pydantic.main.BaseModelRepresents the result of a course in the PESU Academy system.
- Parameters:
code (str)
title (str)
credits (pesuacademy.models.results.Credits | None)
assessments (list[pesuacademy.models.results.Assessment])
- assessments
List of assessments associated with the course.
- Type:
List[Assessment]
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'assessments': FieldInfo(annotation=list[Assessment], required=True), 'code': FieldInfo(annotation=str, required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
-
assessments:
list[Assessment]
- class pesuacademy.models.Credits(**data)[source]
Bases:
pydantic.main.BaseModelRepresents credits information for a course in the PESU Academy system.
- __copy__()
Returns a shallow copy of the model.
- Return type:
Self
- __deepcopy__(memo=None)
Returns a deep copy of the model.
- classmethod __get_pydantic_json_schema__(core_schema, handler, /)
Hook into generating the model’s JSON schema.
- Parameters:
core_schema (
Union[InvalidSchema,AnySchema,NoneSchema,BoolSchema,IntSchema,FloatSchema,DecimalSchema,StringSchema,BytesSchema,DateSchema,TimeSchema,DatetimeSchema,TimedeltaSchema,LiteralSchema,EnumSchema,IsInstanceSchema,IsSubclassSchema,CallableSchema,ListSchema,TupleSchema,SetSchema,FrozenSetSchema,GeneratorSchema,DictSchema,AfterValidatorFunctionSchema,BeforeValidatorFunctionSchema,WrapValidatorFunctionSchema,PlainValidatorFunctionSchema,WithDefaultSchema,NullableSchema,UnionSchema,TaggedUnionSchema,ChainSchema,LaxOrStrictSchema,JsonOrPythonSchema,TypedDictSchema,ModelFieldsSchema,ModelSchema,DataclassArgsSchema,DataclassSchema,ArgumentsSchema,ArgumentsV3Schema,CallSchema,CustomErrorSchema,JsonSchema,UrlSchema,MultiHostUrlSchema,DefinitionsSchema,DefinitionReferenceSchema,UuidSchema,ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.handler (
GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.
- Return type:
- Returns:
A JSON schema, as a Python object.
- __init__(**data)
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- Parameters:
data (Any)
- Return type:
None
- __pretty__(fmt, **kwargs)
Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- classmethod __pydantic_init_subclass__(**kwargs)
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.
- __repr_recursion__(object)
Returns the string representation of a recursive object.
- __rich_repr__()
Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- classmethod construct(_fields_set=None, **values)
- copy(*, include=None, exclude=None, update=None, deep=False)
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
- Return type:
- Parameters:
include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
models_as_dict (bool)
dumps_kwargs (Any)
- model_computed_fields = {}
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.
- !!! note
model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set[str] |None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.values (
Any) – Trusted or pre-validated data dictionary.
- Return type:
Self- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update=None, deep=False)
- !!! abstract “Usage Documentation”
[model_copy](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union[Literal['json','python'],str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to include in the output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – A set of fields to exclude from the output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
- !!! abstract “Usage Documentation”
[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (
int|None) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to include in the JSON output.exclude (
Union[set[int],set[str],Mapping[int,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],Mapping[str,Union[set[int],set[str],Mapping[int,Union[IncEx,bool]],Mapping[str,Union[IncEx,bool]],bool]],None]) – Field(s) to exclude from the JSON output.context (
Any|None) – Additional context to pass to the serializer.by_alias (
bool|None) – Whether to serialize using field aliases.exclude_unset (
bool) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool) – Whether to exclude fields that are set to their default value.exclude_none (
bool) – Whether to exclude fields that have a value of None.round_trip (
bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union[bool,Literal['none','warn','error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].fallback (
Optional[Callable[[Any],Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or None if config.extra is not set to “allow”.
- model_fields = {'earned': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=str, required=True)}
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool) – Whether to use attribute aliases or not.ref_template (
str) – The reference template.schema_generator (
type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modificationsmode (
Literal['validation','serialization']) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple[type[Any],...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.- Return type:
- Returns:
String representing the new class where params are passed to cls as type variables.
- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool) – Whether to force the rebuilding of the model schema, defaults to False.raise_errors (
bool) – Whether to raise errors, defaults to True._parent_namespace_depth (
int) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping[str,Any] |None) – The types namespace, defaults to None.
- Return type:
- Returns:
Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)
Validate a pydantic model instance.
- Parameters:
obj (
Any) – The object to validate.from_attributes (
bool|None) – Whether to extract data from object attributes.context (
Any|None) – Additional context to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str|bytes|bytearray) – The JSON data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any) – The object containing string data to validate.context (
Any|None) – Extra variables to pass to the validator.by_alias (
bool|None) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool|None) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- Return type:
Self- Parameters:
path (str | pathlib.Path)
content_type (str | None)
encoding (str)
proto (pydantic.deprecated.parse.Protocol | None)
allow_pickle (bool)
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
- classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)